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<a href="_kernel_s_g_d_trainer_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="comment">//===========================================================================</span><span class="comment"></span></div>
<div class="line"><a id="l00002" name="l00002"></a><span class="lineno">    2</span><span class="comment">/*!</span></div>
<div class="line"><a id="l00003" name="l00003"></a><span class="lineno">    3</span><span class="comment"> *</span></div>
<div class="line"><a id="l00004" name="l00004"></a><span class="lineno">    4</span><span class="comment"> *</span></div>
<div class="line"><a id="l00005" name="l00005"></a><span class="lineno">    5</span><span class="comment"> * \brief       Generic stochastic gradient descent training for kernel-based models.</span></div>
<div class="line"><a id="l00006" name="l00006"></a><span class="lineno">    6</span><span class="comment"> *</span></div>
<div class="line"><a id="l00007" name="l00007"></a><span class="lineno">    7</span><span class="comment"> *</span></div>
<div class="line"><a id="l00008" name="l00008"></a><span class="lineno">    8</span><span class="comment"> *</span></div>
<div class="line"><a id="l00009" name="l00009"></a><span class="lineno">    9</span><span class="comment"> *</span></div>
<div class="line"><a id="l00010" name="l00010"></a><span class="lineno">   10</span><span class="comment"> * \author      T. Glasmachers</span></div>
<div class="line"><a id="l00011" name="l00011"></a><span class="lineno">   11</span><span class="comment"> * \date        2013</span></div>
<div class="line"><a id="l00012" name="l00012"></a><span class="lineno">   12</span><span class="comment"> *</span></div>
<div class="line"><a id="l00013" name="l00013"></a><span class="lineno">   13</span><span class="comment"> *</span></div>
<div class="line"><a id="l00014" name="l00014"></a><span class="lineno">   14</span><span class="comment"> * \par Copyright 1995-2017 Shark Development Team</span></div>
<div class="line"><a id="l00015" name="l00015"></a><span class="lineno">   15</span><span class="comment"> *</span></div>
<div class="line"><a id="l00016" name="l00016"></a><span class="lineno">   16</span><span class="comment"> * &lt;BR&gt;&lt;HR&gt;</span></div>
<div class="line"><a id="l00017" name="l00017"></a><span class="lineno">   17</span><span class="comment"> * This file is part of Shark.</span></div>
<div class="line"><a id="l00018" name="l00018"></a><span class="lineno">   18</span><span class="comment"> * &lt;https://shark-ml.github.io/Shark/&gt;</span></div>
<div class="line"><a id="l00019" name="l00019"></a><span class="lineno">   19</span><span class="comment"> *</span></div>
<div class="line"><a id="l00020" name="l00020"></a><span class="lineno">   20</span><span class="comment"> * Shark is free software: you can redistribute it and/or modify</span></div>
<div class="line"><a id="l00021" name="l00021"></a><span class="lineno">   21</span><span class="comment"> * it under the terms of the GNU Lesser General Public License as published</span></div>
<div class="line"><a id="l00022" name="l00022"></a><span class="lineno">   22</span><span class="comment"> * by the Free Software Foundation, either version 3 of the License, or</span></div>
<div class="line"><a id="l00023" name="l00023"></a><span class="lineno">   23</span><span class="comment"> * (at your option) any later version.</span></div>
<div class="line"><a id="l00024" name="l00024"></a><span class="lineno">   24</span><span class="comment"> *</span></div>
<div class="line"><a id="l00025" name="l00025"></a><span class="lineno">   25</span><span class="comment"> * Shark is distributed in the hope that it will be useful,</span></div>
<div class="line"><a id="l00026" name="l00026"></a><span class="lineno">   26</span><span class="comment"> * but WITHOUT ANY WARRANTY; without even the implied warranty of</span></div>
<div class="line"><a id="l00027" name="l00027"></a><span class="lineno">   27</span><span class="comment"> * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the</span></div>
<div class="line"><a id="l00028" name="l00028"></a><span class="lineno">   28</span><span class="comment"> * GNU Lesser General Public License for more details.</span></div>
<div class="line"><a id="l00029" name="l00029"></a><span class="lineno">   29</span><span class="comment"> *</span></div>
<div class="line"><a id="l00030" name="l00030"></a><span class="lineno">   30</span><span class="comment"> * You should have received a copy of the GNU Lesser General Public License</span></div>
<div class="line"><a id="l00031" name="l00031"></a><span class="lineno">   31</span><span class="comment"> * along with Shark.  If not, see &lt;http://www.gnu.org/licenses/&gt;.</span></div>
<div class="line"><a id="l00032" name="l00032"></a><span class="lineno">   32</span><span class="comment"> *</span></div>
<div class="line"><a id="l00033" name="l00033"></a><span class="lineno">   33</span><span class="comment"> */</span></div>
<div class="line"><a id="l00034" name="l00034"></a><span class="lineno">   34</span><span class="comment">//===========================================================================</span></div>
<div class="line"><a id="l00035" name="l00035"></a><span class="lineno">   35</span> </div>
<div class="line"><a id="l00036" name="l00036"></a><span class="lineno">   36</span> </div>
<div class="line"><a id="l00037" name="l00037"></a><span class="lineno">   37</span><span class="preprocessor">#ifndef SHARK_ALGORITHMS_KERNELSGDTRAINER_H</span></div>
<div class="line"><a id="l00038" name="l00038"></a><span class="lineno">   38</span><span class="preprocessor">#define SHARK_ALGORITHMS_KERNELSGDTRAINER_H</span></div>
<div class="line"><a id="l00039" name="l00039"></a><span class="lineno">   39</span> </div>
<div class="line"><a id="l00040" name="l00040"></a><span class="lineno">   40</span> </div>
<div class="line"><a id="l00041" name="l00041"></a><span class="lineno">   41</span><span class="preprocessor">#include &lt;<a class="code" href="_abstract_trainer_8h.html" title="Abstract Trainer Interface.">shark/Algorithms/Trainers/AbstractTrainer.h</a>&gt;</span></div>
<div class="line"><a id="l00042" name="l00042"></a><span class="lineno">   42</span><span class="preprocessor">#include &lt;<a class="code" href="_i_parameterizable_8h.html">shark/Core/IParameterizable.h</a>&gt;</span></div>
<div class="line"><a id="l00043" name="l00043"></a><span class="lineno">   43</span><span class="preprocessor">#include &lt;<a class="code" href="_kernel_matrix_8h.html">shark/LinAlg/KernelMatrix.h</a>&gt;</span></div>
<div class="line"><a id="l00044" name="l00044"></a><span class="lineno">   44</span><span class="preprocessor">#include &lt;<a class="code" href="_partly_precomputed_matrix_8h.html">shark/LinAlg/PartlyPrecomputedMatrix.h</a>&gt;</span></div>
<div class="line"><a id="l00045" name="l00045"></a><span class="lineno">   45</span><span class="preprocessor">#include &lt;<a class="code" href="_kernel_expansion_8h.html">shark/Models/Kernels/KernelExpansion.h</a>&gt;</span></div>
<div class="line"><a id="l00046" name="l00046"></a><span class="lineno">   46</span><span class="preprocessor">#include &lt;<a class="code" href="_kernel_helpers_8h.html">shark/Models/Kernels/KernelHelpers.h</a>&gt;</span></div>
<div class="line"><a id="l00047" name="l00047"></a><span class="lineno">   47</span><span class="preprocessor">#include &lt;<a class="code" href="_abstract_loss_8h.html" title="super class of all loss functions">shark/ObjectiveFunctions/Loss/AbstractLoss.h</a>&gt;</span></div>
<div class="line"><a id="l00048" name="l00048"></a><span class="lineno">   48</span> </div>
<div class="line"><a id="l00049" name="l00049"></a><span class="lineno">   49</span> </div>
<div class="line"><a id="l00050" name="l00050"></a><span class="lineno">   50</span><span class="keyword">namespace </span><a class="code hl_namespace" href="namespaceshark.html" title="AbstractMultiObjectiveOptimizer.">shark</a></div>
<div class="line"><a id="l00051" name="l00051"></a><span class="lineno">   51</span>{</div>
<div class="line"><a id="l00052" name="l00052"></a><span class="lineno">   52</span> </div>
<div class="line"><a id="l00053" name="l00053"></a><span class="lineno">   53</span><span class="comment"></span> </div>
<div class="line"><a id="l00054" name="l00054"></a><span class="lineno">   54</span><span class="comment">///</span></div>
<div class="line"><a id="l00055" name="l00055"></a><span class="lineno">   55</span><span class="comment">/// \brief Generic stochastic gradient descent training for kernel-based models.</span></div>
<div class="line"><a id="l00056" name="l00056"></a><span class="lineno">   56</span><span class="comment">///</span></div>
<div class="line"><a id="l00057" name="l00057"></a><span class="lineno">   57</span><span class="comment">/// Given a differentiable loss function L(f, y) for classification</span></div>
<div class="line"><a id="l00058" name="l00058"></a><span class="lineno">   58</span><span class="comment">/// this trainer solves the regularized risk minimization problem</span></div>
<div class="line"><a id="l00059" name="l00059"></a><span class="lineno">   59</span><span class="comment">/// \f[</span></div>
<div class="line"><a id="l00060" name="l00060"></a><span class="lineno">   60</span><span class="comment">///     \min \frac{1}{2} \sum_j \|w_j\|^2 + C \sum_i L(y_i, f(x_i)),</span></div>
<div class="line"><a id="l00061" name="l00061"></a><span class="lineno">   61</span><span class="comment">/// \f]</span></div>
<div class="line"><a id="l00062" name="l00062"></a><span class="lineno">   62</span><span class="comment">/// where i runs over training data, j over classes, and C &gt; 0 is the</span></div>
<div class="line"><a id="l00063" name="l00063"></a><span class="lineno">   63</span><span class="comment">/// regularization parameter.</span></div>
<div class="line"><a id="l00064" name="l00064"></a><span class="lineno">   64</span><span class="comment">///</span></div>
<div class="line"><a id="l00065" name="l00065"></a><span class="lineno">   65</span><span class="comment">/// \par</span></div>
<div class="line"><a id="l00066" name="l00066"></a><span class="lineno">   66</span><span class="comment">/// This implementation is an adaptation of the PEGASOS algorithm, see the paper</span></div>
<div class="line"><a id="l00067" name="l00067"></a><span class="lineno">   67</span><span class="comment">/// &lt;i&gt;Shalev-Shwartz et al. &quot;Pegasos: Primal estimated sub-gradient solver for SVM.&quot; Mathematical Programming 127.1 (2011): 3-30.&lt;/i&gt;&lt;br/&gt;&lt;br/&gt;</span></div>
<div class="line"><a id="l00068" name="l00068"></a><span class="lineno">   68</span><span class="comment">/// However, the (non-essential) projection step is dropped, and the</span></div>
<div class="line"><a id="l00069" name="l00069"></a><span class="lineno">   69</span><span class="comment">/// algorithm is applied to a kernelized model. The resulting</span></div>
<div class="line"><a id="l00070" name="l00070"></a><span class="lineno">   70</span><span class="comment">/// optimization scheme amounts to plain standard stochastic gradient</span></div>
<div class="line"><a id="l00071" name="l00071"></a><span class="lineno">   71</span><span class="comment">/// descent (SGD) with update steps of the form</span></div>
<div class="line"><a id="l00072" name="l00072"></a><span class="lineno">   72</span><span class="comment">/// \f[</span></div>
<div class="line"><a id="l00073" name="l00073"></a><span class="lineno">   73</span><span class="comment">///     w_j \leftarrow (1 - 1/t) w_j + \frac{C}{t} \frac{\partial L(y_i, f(x_i))}{\partial w_j}</span></div>
<div class="line"><a id="l00074" name="l00074"></a><span class="lineno">   74</span><span class="comment">/// \f]</span></div>
<div class="line"><a id="l00075" name="l00075"></a><span class="lineno">   75</span><span class="comment">/// for random index i. The only notable trick borrowed from that paper</span></div>
<div class="line"><a id="l00076" name="l00076"></a><span class="lineno">   76</span><span class="comment">/// is the representation of the weight vectors in the form</span></div>
<div class="line"><a id="l00077" name="l00077"></a><span class="lineno">   77</span><span class="comment">/// \f[</span></div>
<div class="line"><a id="l00078" name="l00078"></a><span class="lineno">   78</span><span class="comment">///     w_j = s \cdot \sum_i \alpha_{i,j} k(x_i, \cdot)</span></div>
<div class="line"><a id="l00079" name="l00079"></a><span class="lineno">   79</span><span class="comment">/// \f]</span></div>
<div class="line"><a id="l00080" name="l00080"></a><span class="lineno">   80</span><span class="comment">/// with a scalar factor s (called alphaScale in the code). This enables</span></div>
<div class="line"><a id="l00081" name="l00081"></a><span class="lineno">   81</span><span class="comment">/// scaling with factor (1 - 1/t) in constant time.</span></div>
<div class="line"><a id="l00082" name="l00082"></a><span class="lineno">   82</span><span class="comment">///</span></div>
<div class="line"><a id="l00083" name="l00083"></a><span class="lineno">   83</span><span class="comment">/// \par</span></div>
<div class="line"><a id="l00084" name="l00084"></a><span class="lineno">   84</span><span class="comment">/// NOTE: Being an SGD-based solver, this algorithm is relatively fast for</span></div>
<div class="line"><a id="l00085" name="l00085"></a><span class="lineno">   85</span><span class="comment">/// differentiable loss functions such as the logistic loss (class CrossEntropy).</span></div>
<div class="line"><a id="l00086" name="l00086"></a><span class="lineno">   86</span><span class="comment">/// It suffers from significantly slower convergence for non-differentiable</span></div>
<div class="line"><a id="l00087" name="l00087"></a><span class="lineno">   87</span><span class="comment">/// losses, e.g., the hinge loss for SVM training.</span></div>
<div class="line"><a id="l00088" name="l00088"></a><span class="lineno">   88</span><span class="comment">/// \ingroup supervised_trainer</span></div>
<div class="line"><a id="l00089" name="l00089"></a><span class="lineno">   89</span><span class="comment"></span><span class="keyword">template</span> &lt;<span class="keyword">class</span> InputType, <span class="keyword">class</span> CacheType = <span class="keywordtype">float</span>&gt;</div>
<div class="foldopen" id="foldopen00090" data-start="{" data-end="};">
<div class="line"><a id="l00090" name="l00090"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_s_g_d_trainer.html">   90</a></span><span class="keyword">class </span><a class="code hl_class" href="classshark_1_1_kernel_s_g_d_trainer.html" title="Generic stochastic gradient descent training for kernel-based models.">KernelSGDTrainer</a> : <span class="keyword">public</span> <a class="code hl_class" href="classshark_1_1_abstract_trainer.html" title="Superclass of supervised learning algorithms.">AbstractTrainer</a>&lt; KernelClassifier&lt;InputType&gt; &gt;, <span class="keyword">public</span> <a class="code hl_class" href="classshark_1_1_i_parameterizable.html" title="Top level interface for everything that holds parameters.">IParameterizable</a>&lt;&gt;</div>
<div class="line"><a id="l00091" name="l00091"></a><span class="lineno">   91</span>{</div>
<div class="line"><a id="l00092" name="l00092"></a><span class="lineno">   92</span><span class="keyword">public</span>:</div>
<div class="line"><a id="l00093" name="l00093"></a><span class="lineno">   93</span>    <span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_abstract_trainer.html" title="Superclass of supervised learning algorithms.">AbstractTrainer&lt; KernelExpansion&lt;InputType&gt;</a> &gt; <a class="code hl_class" href="classshark_1_1_abstract_trainer.html" title="Superclass of supervised learning algorithms.">base_type</a>;</div>
<div class="line"><a id="l00094" name="l00094"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_s_g_d_trainer.html#a606dc86cf240c1fe43791df0ce24998e">   94</a></span>    <span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_abstract_kernel_function.html" title="Base class of all Kernel functions.">AbstractKernelFunction&lt;InputType&gt;</a> <a class="code hl_typedef" href="classshark_1_1_kernel_s_g_d_trainer.html#a606dc86cf240c1fe43791df0ce24998e">KernelType</a>;</div>
<div class="line"><a id="l00095" name="l00095"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_s_g_d_trainer.html#a5488cda990c715d893dfba057fa88b40">   95</a></span>    <span class="keyword">typedef</span> <a class="code hl_struct" href="structshark_1_1_kernel_classifier.html" title="Linear classifier in a kernel feature space.">KernelClassifier&lt;InputType&gt;</a> <a class="code hl_typedef" href="classshark_1_1_kernel_s_g_d_trainer.html#a5488cda990c715d893dfba057fa88b40">ClassifierType</a>;</div>
<div class="line"><a id="l00096" name="l00096"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_s_g_d_trainer.html#a438d0be0a607dac285103e8a264705ae">   96</a></span>    <span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_kernel_expansion.html" title="Linear model in a kernel feature space.">KernelExpansion&lt;InputType&gt;</a> <a class="code hl_typedef" href="classshark_1_1_kernel_s_g_d_trainer.html#a438d0be0a607dac285103e8a264705ae">ModelType</a>;</div>
<div class="line"><a id="l00097" name="l00097"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_s_g_d_trainer.html#afcfe228a0df389b8f56733d0e9ec90dc">   97</a></span>    <span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_abstract_loss.html" title="Loss function interface.">AbstractLoss&lt;unsigned int, RealVector&gt;</a> <a class="code hl_typedef" href="classshark_1_1_kernel_s_g_d_trainer.html#afcfe228a0df389b8f56733d0e9ec90dc">LossType</a>;</div>
<div class="line"><a id="l00098" name="l00098"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_s_g_d_trainer.html#a74cd4fda9509abea240aa92b0475cd19">   98</a></span>    <span class="keyword">typedef</span> <span class="keyword">typename</span> <a class="code hl_struct" href="structshark_1_1_const_proxy_reference.html" title="sets the type of ProxxyReference">ConstProxyReference&lt;typename Batch&lt;InputType&gt;::type</a> <span class="keyword">const</span>&gt;::type <a class="code hl_typedef" href="classshark_1_1_kernel_s_g_d_trainer.html#a74cd4fda9509abea240aa92b0475cd19">ConstBatchInputReference</a>;</div>
<div class="line"><a id="l00099" name="l00099"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_s_g_d_trainer.html#a6097a25678eae9b9152d2b906157dea8">   99</a></span>    <span class="keyword">typedef</span> CacheType <a class="code hl_typedef" href="classshark_1_1_kernel_s_g_d_trainer.html#a6097a25678eae9b9152d2b906157dea8">QpFloatType</a>;</div>
<div class="line"><a id="l00100" name="l00100"></a><span class="lineno">  100</span> </div>
<div class="line"><a id="l00101" name="l00101"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_s_g_d_trainer.html#a65ceba570e60a98afd4669f648332674">  101</a></span>    <span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_kernel_matrix.html" title="Kernel Gram matrix.">KernelMatrix&lt;InputType, QpFloatType&gt;</a> <a class="code hl_typedef" href="classshark_1_1_kernel_s_g_d_trainer.html#a65ceba570e60a98afd4669f648332674">KernelMatrixType</a>;</div>
<div class="line"><a id="l00102" name="l00102"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_s_g_d_trainer.html#adf916f6e4f7e0593ba14ef630f9a449d">  102</a></span>    <span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_partly_precomputed_matrix.html" title="Partly Precomputed version of a matrix for quadratic programming.">PartlyPrecomputedMatrix&lt; KernelMatrixType &gt;</a> <a class="code hl_typedef" href="classshark_1_1_kernel_s_g_d_trainer.html#adf916f6e4f7e0593ba14ef630f9a449d">PartlyPrecomputedMatrixType</a>;</div>
<div class="line"><a id="l00103" name="l00103"></a><span class="lineno">  103</span> </div>
<div class="line"><a id="l00104" name="l00104"></a><span class="lineno">  104</span><span class="comment"></span> </div>
<div class="line"><a id="l00105" name="l00105"></a><span class="lineno">  105</span><span class="comment">    /// \brief Constructor</span></div>
<div class="line"><a id="l00106" name="l00106"></a><span class="lineno">  106</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00107" name="l00107"></a><span class="lineno">  107</span><span class="comment">    /// \param  kernel          kernel function to use for training and prediction</span></div>
<div class="line"><a id="l00108" name="l00108"></a><span class="lineno">  108</span><span class="comment">    /// \param  loss            (sub-)differentiable loss function</span></div>
<div class="line"><a id="l00109" name="l00109"></a><span class="lineno">  109</span><span class="comment">    /// \param  C               regularization parameter - always the &#39;true&#39; value of C, even when unconstrained is set</span></div>
<div class="line"><a id="l00110" name="l00110"></a><span class="lineno">  110</span><span class="comment">    /// \param  offset          whether to train with offset/bias parameter or not</span></div>
<div class="line"><a id="l00111" name="l00111"></a><span class="lineno">  111</span><span class="comment">    /// \param  unconstrained   when a C-value is given via setParameter, should it be piped through the exp-function before using it in the solver?</span></div>
<div class="line"><a id="l00112" name="l00112"></a><span class="lineno">  112</span><span class="comment">    /// \param  cacheSize       size of the cache</span></div>
<div class="foldopen" id="foldopen00113" data-start="{" data-end="}">
<div class="line"><a id="l00113" name="l00113"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_s_g_d_trainer.html#adc23910b5eddd9a8190fe796ff9291f4">  113</a></span><span class="comment"></span>    <a class="code hl_function" href="classshark_1_1_kernel_s_g_d_trainer.html#adc23910b5eddd9a8190fe796ff9291f4" title="Constructor.">KernelSGDTrainer</a>(<a class="code hl_class" href="classshark_1_1_abstract_kernel_function.html">KernelType</a>* <a class="code hl_function" href="classshark_1_1_kernel_s_g_d_trainer.html#ade8ab81af9b2934ae72b442b8c98e9d5" title="get the kernel function">kernel</a>, <span class="keyword">const</span> <a class="code hl_class" href="classshark_1_1_abstract_loss.html">LossType</a>* loss, <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_kernel_s_g_d_trainer.html#a442f481e2e8f97156994ed478ec5d91f" title="return the value of the regularization parameter">C</a>, <span class="keywordtype">bool</span> offset, <span class="keywordtype">bool</span> unconstrained = <span class="keyword">false</span>, <span class="keywordtype">size_t</span> <a class="code hl_function" href="classshark_1_1_kernel_s_g_d_trainer.html#ac0183963a7f82e77ba7333666e05f895" title="return current cachesize">cacheSize</a> = 0x4000000)</div>
<div class="line"><a id="l00114" name="l00114"></a><span class="lineno">  114</span>        : <a class="code hl_variable" href="classshark_1_1_kernel_s_g_d_trainer.html#ada7a1e197b8d2721ac531c68988dfd17" title="pointer to kernel function">m_kernel</a>(<a class="code hl_function" href="classshark_1_1_kernel_s_g_d_trainer.html#ade8ab81af9b2934ae72b442b8c98e9d5" title="get the kernel function">kernel</a>)</div>
<div class="line"><a id="l00115" name="l00115"></a><span class="lineno">  115</span>        , <a class="code hl_variable" href="classshark_1_1_kernel_s_g_d_trainer.html#ada9d9dbb9f3abd4cbf09c53e0bb92fd6" title="pointer to loss function">m_loss</a>(loss)</div>
<div class="line"><a id="l00116" name="l00116"></a><span class="lineno">  116</span>        , <a class="code hl_variable" href="classshark_1_1_kernel_s_g_d_trainer.html#a374547615c259b8b19ca846fac2a9753" title="regularization parameter">m_C</a>(<a class="code hl_function" href="classshark_1_1_kernel_s_g_d_trainer.html#a442f481e2e8f97156994ed478ec5d91f" title="return the value of the regularization parameter">C</a>)</div>
<div class="line"><a id="l00117" name="l00117"></a><span class="lineno">  117</span>        , <a class="code hl_variable" href="classshark_1_1_kernel_s_g_d_trainer.html#a2222af2e7de2a6c68b18678f0fff1cff" title="should the resulting model have an offset term?">m_offset</a>(offset)</div>
<div class="line"><a id="l00118" name="l00118"></a><span class="lineno">  118</span>        , <a class="code hl_variable" href="classshark_1_1_kernel_s_g_d_trainer.html#acc37f18ac6b0d7bf0a526f960ad4c4b6" title="should C be stored as log(C) as a parameter?">m_unconstrained</a>(unconstrained)</div>
<div class="line"><a id="l00119" name="l00119"></a><span class="lineno">  119</span>        , <a class="code hl_variable" href="classshark_1_1_kernel_s_g_d_trainer.html#a0230c6c90039fd6dbc20dd5192c69c8b" title="number of training epochs (sweeps over the data), or 0 for default = max(10, C)">m_epochs</a>(0)</div>
<div class="line"><a id="l00120" name="l00120"></a><span class="lineno">  120</span>        , <a class="code hl_variable" href="classshark_1_1_kernel_s_g_d_trainer.html#a3266b159eb84e746be2d35ff719c26bd">m_cacheSize</a>(<a class="code hl_function" href="classshark_1_1_kernel_s_g_d_trainer.html#ac0183963a7f82e77ba7333666e05f895" title="return current cachesize">cacheSize</a>)</div>
<div class="line"><a id="l00121" name="l00121"></a><span class="lineno">  121</span>    { }</div>
</div>
<div class="line"><a id="l00122" name="l00122"></a><span class="lineno">  122</span> </div>
<div class="line"><a id="l00123" name="l00123"></a><span class="lineno">  123</span><span class="comment"></span> </div>
<div class="line"><a id="l00124" name="l00124"></a><span class="lineno">  124</span><span class="comment">    /// return current cachesize</span></div>
<div class="foldopen" id="foldopen00125" data-start="{" data-end="}">
<div class="line"><a id="l00125" name="l00125"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_s_g_d_trainer.html#ac0183963a7f82e77ba7333666e05f895">  125</a></span><span class="comment"></span>    <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_kernel_s_g_d_trainer.html#ac0183963a7f82e77ba7333666e05f895" title="return current cachesize">cacheSize</a>()<span class="keyword"> const</span></div>
<div class="line"><a id="l00126" name="l00126"></a><span class="lineno">  126</span><span class="keyword">    </span>{</div>
<div class="line"><a id="l00127" name="l00127"></a><span class="lineno">  127</span>        <span class="keywordflow">return</span> <a class="code hl_variable" href="classshark_1_1_kernel_s_g_d_trainer.html#a3266b159eb84e746be2d35ff719c26bd">m_cacheSize</a>;</div>
<div class="line"><a id="l00128" name="l00128"></a><span class="lineno">  128</span>    }</div>
</div>
<div class="line"><a id="l00129" name="l00129"></a><span class="lineno">  129</span> </div>
<div class="line"><a id="l00130" name="l00130"></a><span class="lineno">  130</span> </div>
<div class="foldopen" id="foldopen00131" data-start="{" data-end="}">
<div class="line"><a id="l00131" name="l00131"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_s_g_d_trainer.html#ab17a0f3738e21dc766e74cfd6e8e05ff">  131</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_kernel_s_g_d_trainer.html#ab17a0f3738e21dc766e74cfd6e8e05ff">setCacheSize</a>(std::size_t size)</div>
<div class="line"><a id="l00132" name="l00132"></a><span class="lineno">  132</span>    {</div>
<div class="line"><a id="l00133" name="l00133"></a><span class="lineno">  133</span>        <a class="code hl_variable" href="classshark_1_1_kernel_s_g_d_trainer.html#a3266b159eb84e746be2d35ff719c26bd">m_cacheSize</a> = size;</div>
<div class="line"><a id="l00134" name="l00134"></a><span class="lineno">  134</span>    }</div>
</div>
<div class="line"><a id="l00135" name="l00135"></a><span class="lineno">  135</span><span class="comment"></span> </div>
<div class="line"><a id="l00136" name="l00136"></a><span class="lineno">  136</span><span class="comment">    /// \brief From INameable: return the class name.</span></div>
<div class="foldopen" id="foldopen00137" data-start="{" data-end="}">
<div class="line"><a id="l00137" name="l00137"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_s_g_d_trainer.html#a5adb31bcd362569c1e123a6325329be9">  137</a></span><span class="comment"></span>    std::string <a class="code hl_function" href="classshark_1_1_kernel_s_g_d_trainer.html#a5adb31bcd362569c1e123a6325329be9" title="From INameable: return the class name.">name</a>()<span class="keyword"> const</span></div>
<div class="line"><a id="l00138" name="l00138"></a><span class="lineno">  138</span><span class="keyword">    </span>{ <span class="keywordflow">return</span> <span class="stringliteral">&quot;KernelSGDTrainer&quot;</span>; }</div>
</div>
<div class="line"><a id="l00139" name="l00139"></a><span class="lineno">  139</span> </div>
<div class="foldopen" id="foldopen00140" data-start="{" data-end="}">
<div class="line"><a id="l00140" name="l00140"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_s_g_d_trainer.html#a31c7513f29d280ad3165d18100962391">  140</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_kernel_s_g_d_trainer.html#a31c7513f29d280ad3165d18100962391">train</a>(<a class="code hl_struct" href="structshark_1_1_kernel_classifier.html" title="Linear classifier in a kernel feature space.">ClassifierType</a>&amp; classifier, <span class="keyword">const</span> <a class="code hl_class" href="classshark_1_1_labeled_data.html" title="Data set for supervised learning.">LabeledData&lt;InputType, unsigned int&gt;</a>&amp; dataset)</div>
<div class="line"><a id="l00141" name="l00141"></a><span class="lineno">  141</span>    {</div>
<div class="line"><a id="l00142" name="l00142"></a><span class="lineno">  142</span>        std::size_t ell = dataset.<a class="code hl_function" href="group__shark__globals.html#ga5333445992cd6b14392cd80a1ab5403c" title="Returns the total number of elements.">numberOfElements</a>();</div>
<div class="line"><a id="l00143" name="l00143"></a><span class="lineno">  143</span>        std::size_t classes = <a class="code hl_function" href="group__shark__globals.html#ga1fee3b5830ae11a78109e8c0265c6569" title="Return the number of classes of a set of class labels with unsigned int label encoding.">numberOfClasses</a>(dataset);</div>
<div class="line"><a id="l00144" name="l00144"></a><span class="lineno">  144</span>        <a class="code hl_class" href="classshark_1_1_kernel_expansion.html" title="Linear model in a kernel feature space.">ModelType</a>&amp; model = classifier.<a class="code hl_function" href="classshark_1_1_classifier.html#adf58b2ed9969bad9828772dd23c59c02" title="Return the decision function.">decisionFunction</a>();</div>
<div class="line"><a id="l00145" name="l00145"></a><span class="lineno">  145</span> </div>
<div class="line"><a id="l00146" name="l00146"></a><span class="lineno">  146</span>        model.<a class="code hl_function" href="classshark_1_1_kernel_expansion.html#a38c97766f52bf00e5b0120c46c15f37f">setStructure</a>(<a class="code hl_variable" href="classshark_1_1_kernel_s_g_d_trainer.html#ada7a1e197b8d2721ac531c68988dfd17" title="pointer to kernel function">m_kernel</a>, dataset.<a class="code hl_function" href="group__shark__globals.html#ga6f74e657c7e0c8a32b2456fb328bd653" title="Access to inputs as a separate container.">inputs</a>(), <a class="code hl_variable" href="classshark_1_1_kernel_s_g_d_trainer.html#a2222af2e7de2a6c68b18678f0fff1cff" title="should the resulting model have an offset term?">m_offset</a>, classes);</div>
<div class="line"><a id="l00147" name="l00147"></a><span class="lineno">  147</span> </div>
<div class="line"><a id="l00148" name="l00148"></a><span class="lineno">  148</span>        RealMatrix&amp; alpha = model.<a class="code hl_function" href="classshark_1_1_kernel_expansion.html#a3c65dfd17f38eaa461f6400d302fae48">alpha</a>();</div>
<div class="line"><a id="l00149" name="l00149"></a><span class="lineno">  149</span> </div>
<div class="line"><a id="l00150" name="l00150"></a><span class="lineno">  150</span>        <span class="comment">// pre-compute the kernel matrix (may change in the future)</span></div>
<div class="line"><a id="l00151" name="l00151"></a><span class="lineno">  151</span>        <span class="comment">// and create linear array of labels</span></div>
<div class="line"><a id="l00152" name="l00152"></a><span class="lineno">  152</span>        <a class="code hl_class" href="classshark_1_1_kernel_matrix.html" title="Kernel Gram matrix.">KernelMatrixType</a>  km(*(this-&gt;<a class="code hl_variable" href="classshark_1_1_kernel_s_g_d_trainer.html#ada7a1e197b8d2721ac531c68988dfd17" title="pointer to kernel function">m_kernel</a>), dataset.<a class="code hl_function" href="group__shark__globals.html#ga6f74e657c7e0c8a32b2456fb328bd653" title="Access to inputs as a separate container.">inputs</a>());</div>
<div class="line"><a id="l00153" name="l00153"></a><span class="lineno">  153</span>        <a class="code hl_class" href="classshark_1_1_partly_precomputed_matrix.html" title="Partly Precomputed version of a matrix for quadratic programming.">PartlyPrecomputedMatrixType</a>  K(&amp;km, <a class="code hl_variable" href="classshark_1_1_kernel_s_g_d_trainer.html#a3266b159eb84e746be2d35ff719c26bd">m_cacheSize</a>);</div>
<div class="line"><a id="l00154" name="l00154"></a><span class="lineno">  154</span>        UIntVector y = <a class="code hl_function" href="namespaceshark.html#a5478d144c4c997faf5c246dd8e2f85b8" title="creates a batch from a range of inputs">createBatch</a>(dataset.<a class="code hl_function" href="group__shark__globals.html#ga6328a5aa2570c01a5ac5f25076071663" title="Access to labels as a separate container.">labels</a>().<a class="code hl_function" href="group__shark__globals.html#gad9b0233e3adc882ed94f418f80767b09" title="Returns the range of elements.">elements</a>());</div>
<div class="line"><a id="l00155" name="l00155"></a><span class="lineno">  155</span>        <span class="keyword">const</span> <span class="keywordtype">double</span> lambda = 0.5 / (ell * <a class="code hl_variable" href="classshark_1_1_kernel_s_g_d_trainer.html#a374547615c259b8b19ca846fac2a9753" title="regularization parameter">m_C</a>);</div>
<div class="line"><a id="l00156" name="l00156"></a><span class="lineno">  156</span> </div>
<div class="line"><a id="l00157" name="l00157"></a><span class="lineno">  157</span>        <span class="keywordtype">double</span> alphaScale = 1.0;</div>
<div class="line"><a id="l00158" name="l00158"></a><span class="lineno">  158</span>        std::size_t iterations;</div>
<div class="line"><a id="l00159" name="l00159"></a><span class="lineno">  159</span>        <span class="keywordflow">if</span>(<a class="code hl_variable" href="classshark_1_1_kernel_s_g_d_trainer.html#a0230c6c90039fd6dbc20dd5192c69c8b" title="number of training epochs (sweeps over the data), or 0 for default = max(10, C)">m_epochs</a> == 0) iterations = std::max(10 * ell, std::size_t(std::ceil(<a class="code hl_variable" href="classshark_1_1_kernel_s_g_d_trainer.html#a374547615c259b8b19ca846fac2a9753" title="regularization parameter">m_C</a> * ell)));</div>
<div class="line"><a id="l00160" name="l00160"></a><span class="lineno">  160</span>        <span class="keywordflow">else</span> iterations = <a class="code hl_variable" href="classshark_1_1_kernel_s_g_d_trainer.html#a0230c6c90039fd6dbc20dd5192c69c8b" title="number of training epochs (sweeps over the data), or 0 for default = max(10, C)">m_epochs</a> * ell;</div>
<div class="line"><a id="l00161" name="l00161"></a><span class="lineno">  161</span> </div>
<div class="line"><a id="l00162" name="l00162"></a><span class="lineno">  162</span>        <span class="comment">// preinitialize everything to prevent costly memory allocations in the loop</span></div>
<div class="line"><a id="l00163" name="l00163"></a><span class="lineno">  163</span>        RealVector f_b(classes, 0.0);</div>
<div class="line"><a id="l00164" name="l00164"></a><span class="lineno">  164</span>        RealVector derivative(classes, 0.0);</div>
<div class="line"><a id="l00165" name="l00165"></a><span class="lineno">  165</span> </div>
<div class="line"><a id="l00166" name="l00166"></a><span class="lineno">  166</span>        <span class="comment">// SGD loop</span></div>
<div class="line"><a id="l00167" name="l00167"></a><span class="lineno">  167</span>        blas::vector&lt;QpFloatType&gt; kernelRow(ell, 0);</div>
<div class="line"><a id="l00168" name="l00168"></a><span class="lineno">  168</span>        <span class="keywordflow">for</span>(std::size_t iter = 0; iter &lt; iterations; iter++)</div>
<div class="line"><a id="l00169" name="l00169"></a><span class="lineno">  169</span>        {</div>
<div class="line"><a id="l00170" name="l00170"></a><span class="lineno">  170</span>            <span class="comment">// active variable</span></div>
<div class="line"><a id="l00171" name="l00171"></a><span class="lineno">  171</span>            std::size_t b = <a class="code hl_function" href="namespaceshark_1_1random.html#aa64d4174eaf7111b03e0504eaa56b666" title="Draws a discrete number in {low,low+1,...,high} by drawing random numbers from rng.">random::discrete</a>(<a class="code hl_variable" href="namespaceshark_1_1random.html#ab5c1547eee483974d008d43f621a2234">random::globalRng</a>, std::size_t(0), ell - 1);</div>
<div class="line"><a id="l00172" name="l00172"></a><span class="lineno">  172</span> </div>
<div class="line"><a id="l00173" name="l00173"></a><span class="lineno">  173</span>            <span class="comment">// learning rate</span></div>
<div class="line"><a id="l00174" name="l00174"></a><span class="lineno">  174</span>            <span class="keyword">const</span> <span class="keywordtype">double</span> eta = 1.0 / (lambda * (iter + ell));</div>
<div class="line"><a id="l00175" name="l00175"></a><span class="lineno">  175</span> </div>
<div class="line"><a id="l00176" name="l00176"></a><span class="lineno">  176</span>            <span class="comment">// compute prediction</span></div>
<div class="line"><a id="l00177" name="l00177"></a><span class="lineno">  177</span>            K.<a class="code hl_function" href="classshark_1_1_partly_precomputed_matrix.html#aed8139929435c448eac748cf119ef275">row</a>(b, kernelRow);</div>
<div class="line"><a id="l00178" name="l00178"></a><span class="lineno">  178</span>            noalias(f_b) = alphaScale * prod(trans(alpha), kernelRow);</div>
<div class="line"><a id="l00179" name="l00179"></a><span class="lineno">  179</span>            <span class="keywordflow">if</span>(<a class="code hl_variable" href="classshark_1_1_kernel_s_g_d_trainer.html#a2222af2e7de2a6c68b18678f0fff1cff" title="should the resulting model have an offset term?">m_offset</a>) noalias(f_b) += model.<a class="code hl_function" href="classshark_1_1_kernel_expansion.html#a1c89cb50933ee211d67af90e6366e0ee">offset</a>();</div>
<div class="line"><a id="l00180" name="l00180"></a><span class="lineno">  180</span> </div>
<div class="line"><a id="l00181" name="l00181"></a><span class="lineno">  181</span>            <span class="comment">// stochastic gradient descent (SGD) step</span></div>
<div class="line"><a id="l00182" name="l00182"></a><span class="lineno">  182</span>            derivative.clear();</div>
<div class="line"><a id="l00183" name="l00183"></a><span class="lineno">  183</span>            <a class="code hl_variable" href="classshark_1_1_kernel_s_g_d_trainer.html#ada9d9dbb9f3abd4cbf09c53e0bb92fd6" title="pointer to loss function">m_loss</a>-&gt;<a class="code hl_function" href="classshark_1_1_abstract_loss.html#a71706ed4c40d1635db1c372ecf5c8686" title="evaluate the loss and its derivative for a target and a prediction">evalDerivative</a>(y[b], f_b, derivative);</div>
<div class="line"><a id="l00184" name="l00184"></a><span class="lineno">  184</span> </div>
<div class="line"><a id="l00185" name="l00185"></a><span class="lineno">  185</span>            <span class="comment">// alphaScale *= (1.0 - eta * lambda);</span></div>
<div class="line"><a id="l00186" name="l00186"></a><span class="lineno">  186</span>            alphaScale = (ell - 1.0) / (ell + iter);   <span class="comment">// numerically more stable</span></div>
<div class="line"><a id="l00187" name="l00187"></a><span class="lineno">  187</span> </div>
<div class="line"><a id="l00188" name="l00188"></a><span class="lineno">  188</span>            noalias(row(alpha, b)) -= (eta / alphaScale) * derivative;</div>
<div class="line"><a id="l00189" name="l00189"></a><span class="lineno">  189</span>            <span class="keywordflow">if</span>(<a class="code hl_variable" href="classshark_1_1_kernel_s_g_d_trainer.html#a2222af2e7de2a6c68b18678f0fff1cff" title="should the resulting model have an offset term?">m_offset</a>) noalias(model.<a class="code hl_function" href="classshark_1_1_kernel_expansion.html#a1c89cb50933ee211d67af90e6366e0ee">offset</a>()) -= eta * derivative;</div>
<div class="line"><a id="l00190" name="l00190"></a><span class="lineno">  190</span>        }</div>
<div class="line"><a id="l00191" name="l00191"></a><span class="lineno">  191</span> </div>
<div class="line"><a id="l00192" name="l00192"></a><span class="lineno">  192</span>        alpha *= alphaScale;</div>
<div class="line"><a id="l00193" name="l00193"></a><span class="lineno">  193</span> </div>
<div class="line"><a id="l00194" name="l00194"></a><span class="lineno">  194</span>        <span class="comment">// model.sparsify();</span></div>
<div class="line"><a id="l00195" name="l00195"></a><span class="lineno">  195</span>    }</div>
</div>
<div class="line"><a id="l00196" name="l00196"></a><span class="lineno">  196</span><span class="comment"></span> </div>
<div class="line"><a id="l00197" name="l00197"></a><span class="lineno">  197</span><span class="comment">    /// Return the number of training epochs.</span></div>
<div class="line"><a id="l00198" name="l00198"></a><span class="lineno">  198</span><span class="comment">    /// A value of 0 indicates that the default of max(10, C) should be used.</span></div>
<div class="foldopen" id="foldopen00199" data-start="{" data-end="}">
<div class="line"><a id="l00199" name="l00199"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_s_g_d_trainer.html#ae7b9d884f5389bad153a8d9780cd494f">  199</a></span><span class="comment"></span>    std::size_t <a class="code hl_function" href="classshark_1_1_kernel_s_g_d_trainer.html#ae7b9d884f5389bad153a8d9780cd494f">epochs</a>()<span class="keyword"> const</span></div>
<div class="line"><a id="l00200" name="l00200"></a><span class="lineno">  200</span><span class="keyword">    </span>{ <span class="keywordflow">return</span> <a class="code hl_variable" href="classshark_1_1_kernel_s_g_d_trainer.html#a0230c6c90039fd6dbc20dd5192c69c8b" title="number of training epochs (sweeps over the data), or 0 for default = max(10, C)">m_epochs</a>; }</div>
</div>
<div class="line"><a id="l00201" name="l00201"></a><span class="lineno">  201</span><span class="comment"></span> </div>
<div class="line"><a id="l00202" name="l00202"></a><span class="lineno">  202</span><span class="comment">    /// Set the number of training epochs.</span></div>
<div class="line"><a id="l00203" name="l00203"></a><span class="lineno">  203</span><span class="comment">    /// A value of 0 indicates that the default of max(10, C) should be used.</span></div>
<div class="foldopen" id="foldopen00204" data-start="{" data-end="}">
<div class="line"><a id="l00204" name="l00204"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_s_g_d_trainer.html#acc7519d465974fcbb2c059debcedcc8e">  204</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_kernel_s_g_d_trainer.html#acc7519d465974fcbb2c059debcedcc8e">setEpochs</a>(std::size_t value)</div>
<div class="line"><a id="l00205" name="l00205"></a><span class="lineno">  205</span>    { <a class="code hl_variable" href="classshark_1_1_kernel_s_g_d_trainer.html#a0230c6c90039fd6dbc20dd5192c69c8b" title="number of training epochs (sweeps over the data), or 0 for default = max(10, C)">m_epochs</a> = value; }</div>
</div>
<div class="line"><a id="l00206" name="l00206"></a><span class="lineno">  206</span><span class="comment"></span> </div>
<div class="line"><a id="l00207" name="l00207"></a><span class="lineno">  207</span><span class="comment">    /// get the kernel function</span></div>
<div class="foldopen" id="foldopen00208" data-start="{" data-end="}">
<div class="line"><a id="l00208" name="l00208"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_s_g_d_trainer.html#ade8ab81af9b2934ae72b442b8c98e9d5">  208</a></span><span class="comment"></span>    <a class="code hl_class" href="classshark_1_1_abstract_kernel_function.html">KernelType</a>* <a class="code hl_function" href="classshark_1_1_kernel_s_g_d_trainer.html#ade8ab81af9b2934ae72b442b8c98e9d5" title="get the kernel function">kernel</a>()</div>
<div class="line"><a id="l00209" name="l00209"></a><span class="lineno">  209</span>    { <span class="keywordflow">return</span> <a class="code hl_variable" href="classshark_1_1_kernel_s_g_d_trainer.html#ada7a1e197b8d2721ac531c68988dfd17" title="pointer to kernel function">m_kernel</a>; }<span class="comment"></span></div>
</div>
<div class="line"><a id="l00210" name="l00210"></a><span class="lineno">  210</span><span class="comment">    /// get the kernel function</span></div>
<div class="foldopen" id="foldopen00211" data-start="{" data-end="}">
<div class="line"><a id="l00211" name="l00211"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_s_g_d_trainer.html#ae8547043f53fd4f9a0fabbc0b30633e6">  211</a></span><span class="comment"></span>    <span class="keyword">const</span> <a class="code hl_class" href="classshark_1_1_abstract_kernel_function.html">KernelType</a>* <a class="code hl_function" href="classshark_1_1_kernel_s_g_d_trainer.html#ae8547043f53fd4f9a0fabbc0b30633e6" title="get the kernel function">kernel</a>()<span class="keyword"> const</span></div>
<div class="line"><a id="l00212" name="l00212"></a><span class="lineno">  212</span><span class="keyword">    </span>{ <span class="keywordflow">return</span> <a class="code hl_variable" href="classshark_1_1_kernel_s_g_d_trainer.html#ada7a1e197b8d2721ac531c68988dfd17" title="pointer to kernel function">m_kernel</a>; }<span class="comment"></span></div>
</div>
<div class="line"><a id="l00213" name="l00213"></a><span class="lineno">  213</span><span class="comment">    /// set the kernel function</span></div>
<div class="foldopen" id="foldopen00214" data-start="{" data-end="}">
<div class="line"><a id="l00214" name="l00214"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_s_g_d_trainer.html#ac9b2d2d0ca5dc5bdf44194d1cbffe607">  214</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_kernel_s_g_d_trainer.html#ac9b2d2d0ca5dc5bdf44194d1cbffe607" title="set the kernel function">setKernel</a>(<a class="code hl_class" href="classshark_1_1_abstract_kernel_function.html">KernelType</a>* <a class="code hl_function" href="classshark_1_1_kernel_s_g_d_trainer.html#ade8ab81af9b2934ae72b442b8c98e9d5" title="get the kernel function">kernel</a>)</div>
<div class="line"><a id="l00215" name="l00215"></a><span class="lineno">  215</span>    { <a class="code hl_variable" href="classshark_1_1_kernel_s_g_d_trainer.html#ada7a1e197b8d2721ac531c68988dfd17" title="pointer to kernel function">m_kernel</a> = <a class="code hl_function" href="classshark_1_1_kernel_s_g_d_trainer.html#ade8ab81af9b2934ae72b442b8c98e9d5" title="get the kernel function">kernel</a>; }</div>
</div>
<div class="line"><a id="l00216" name="l00216"></a><span class="lineno">  216</span><span class="comment"></span> </div>
<div class="line"><a id="l00217" name="l00217"></a><span class="lineno">  217</span><span class="comment">    /// check whether the parameter C is represented as log(C), thus,</span></div>
<div class="line"><a id="l00218" name="l00218"></a><span class="lineno">  218</span><span class="comment">    /// in a form suitable for unconstrained optimization, in the</span></div>
<div class="line"><a id="l00219" name="l00219"></a><span class="lineno">  219</span><span class="comment">    /// parameter vector</span></div>
<div class="foldopen" id="foldopen00220" data-start="{" data-end="}">
<div class="line"><a id="l00220" name="l00220"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_s_g_d_trainer.html#a5f52c070bb985c7bf1f7063c7b4c695a">  220</a></span><span class="comment"></span>    <span class="keywordtype">bool</span> <a class="code hl_function" href="classshark_1_1_kernel_s_g_d_trainer.html#a5f52c070bb985c7bf1f7063c7b4c695a">isUnconstrained</a>()<span class="keyword"> const</span></div>
<div class="line"><a id="l00221" name="l00221"></a><span class="lineno">  221</span><span class="keyword">    </span>{ <span class="keywordflow">return</span> <a class="code hl_variable" href="classshark_1_1_kernel_s_g_d_trainer.html#acc37f18ac6b0d7bf0a526f960ad4c4b6" title="should C be stored as log(C) as a parameter?">m_unconstrained</a>; }</div>
</div>
<div class="line"><a id="l00222" name="l00222"></a><span class="lineno">  222</span><span class="comment"></span> </div>
<div class="line"><a id="l00223" name="l00223"></a><span class="lineno">  223</span><span class="comment">    /// return the value of the regularization parameter</span></div>
<div class="foldopen" id="foldopen00224" data-start="{" data-end="}">
<div class="line"><a id="l00224" name="l00224"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_s_g_d_trainer.html#a442f481e2e8f97156994ed478ec5d91f">  224</a></span><span class="comment"></span>    <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_kernel_s_g_d_trainer.html#a442f481e2e8f97156994ed478ec5d91f" title="return the value of the regularization parameter">C</a>()<span class="keyword"> const</span></div>
<div class="line"><a id="l00225" name="l00225"></a><span class="lineno">  225</span><span class="keyword">    </span>{ <span class="keywordflow">return</span> <a class="code hl_variable" href="classshark_1_1_kernel_s_g_d_trainer.html#a374547615c259b8b19ca846fac2a9753" title="regularization parameter">m_C</a>; }</div>
</div>
<div class="line"><a id="l00226" name="l00226"></a><span class="lineno">  226</span><span class="comment"></span> </div>
<div class="line"><a id="l00227" name="l00227"></a><span class="lineno">  227</span><span class="comment">    /// set the value of the regularization parameter (must be positive)</span></div>
<div class="foldopen" id="foldopen00228" data-start="{" data-end="}">
<div class="line"><a id="l00228" name="l00228"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_s_g_d_trainer.html#a7b5805e9aedbf6a043c19aa802255837">  228</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_kernel_s_g_d_trainer.html#a7b5805e9aedbf6a043c19aa802255837" title="set the value of the regularization parameter (must be positive)">setC</a>(<span class="keywordtype">double</span> value)</div>
<div class="line"><a id="l00229" name="l00229"></a><span class="lineno">  229</span>    {</div>
<div class="line"><a id="l00230" name="l00230"></a><span class="lineno">  230</span>        <a class="code hl_define" href="_exception_8h.html#abd848215f138fc44f696aecb3e417e6c">RANGE_CHECK</a>(value &gt; 0.0);</div>
<div class="line"><a id="l00231" name="l00231"></a><span class="lineno">  231</span>        <a class="code hl_variable" href="classshark_1_1_kernel_s_g_d_trainer.html#a374547615c259b8b19ca846fac2a9753" title="regularization parameter">m_C</a> = value;</div>
<div class="line"><a id="l00232" name="l00232"></a><span class="lineno">  232</span>    }</div>
</div>
<div class="line"><a id="l00233" name="l00233"></a><span class="lineno">  233</span><span class="comment"></span> </div>
<div class="line"><a id="l00234" name="l00234"></a><span class="lineno">  234</span><span class="comment">    /// check whether the model to be trained should include an offset term</span></div>
<div class="foldopen" id="foldopen00235" data-start="{" data-end="}">
<div class="line"><a id="l00235" name="l00235"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_s_g_d_trainer.html#a09fa50917fb4f3deda0e6b0c29977d07">  235</a></span><span class="comment"></span>    <span class="keywordtype">bool</span> <a class="code hl_function" href="classshark_1_1_kernel_s_g_d_trainer.html#a09fa50917fb4f3deda0e6b0c29977d07" title="check whether the model to be trained should include an offset term">trainOffset</a>()<span class="keyword"> const</span></div>
<div class="line"><a id="l00236" name="l00236"></a><span class="lineno">  236</span><span class="keyword">    </span>{ <span class="keywordflow">return</span> <a class="code hl_variable" href="classshark_1_1_kernel_s_g_d_trainer.html#a2222af2e7de2a6c68b18678f0fff1cff" title="should the resulting model have an offset term?">m_offset</a>; }</div>
</div>
<div class="line"><a id="l00237" name="l00237"></a><span class="lineno">  237</span><span class="comment"></span> </div>
<div class="line"><a id="l00238" name="l00238"></a><span class="lineno">  238</span><span class="comment">    ///\brief  Returns the vector of hyper-parameters.</span></div>
<div class="foldopen" id="foldopen00239" data-start="{" data-end="}">
<div class="line"><a id="l00239" name="l00239"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_s_g_d_trainer.html#ab9e4920082ba0a7a88a9589d82024326">  239</a></span><span class="comment"></span>    RealVector <a class="code hl_function" href="classshark_1_1_kernel_s_g_d_trainer.html#ab9e4920082ba0a7a88a9589d82024326" title="Returns the vector of hyper-parameters.">parameterVector</a>()<span class="keyword"> const</span>{</div>
<div class="line"><a id="l00240" name="l00240"></a><span class="lineno">  240</span>        <span class="keywordtype">double</span> parC= <a class="code hl_variable" href="classshark_1_1_kernel_s_g_d_trainer.html#acc37f18ac6b0d7bf0a526f960ad4c4b6" title="should C be stored as log(C) as a parameter?">m_unconstrained</a>? std::log(<a class="code hl_variable" href="classshark_1_1_kernel_s_g_d_trainer.html#a374547615c259b8b19ca846fac2a9753" title="regularization parameter">m_C</a>): <a class="code hl_variable" href="classshark_1_1_kernel_s_g_d_trainer.html#a374547615c259b8b19ca846fac2a9753" title="regularization parameter">m_C</a>;</div>
<div class="line"><a id="l00241" name="l00241"></a><span class="lineno">  241</span>        <span class="keywordflow">return</span> <a class="code hl_variable" href="classshark_1_1_kernel_s_g_d_trainer.html#ada7a1e197b8d2721ac531c68988dfd17" title="pointer to kernel function">m_kernel</a>-&gt;<a class="code hl_function" href="classshark_1_1_i_parameterizable.html#afaa2ba692ab64a0edbff60d7ee6794db" title="Return the parameter vector.">parameterVector</a>() | parC;</div>
<div class="line"><a id="l00242" name="l00242"></a><span class="lineno">  242</span>    }</div>
</div>
<div class="line"><a id="l00243" name="l00243"></a><span class="lineno">  243</span><span class="comment"></span> </div>
<div class="line"><a id="l00244" name="l00244"></a><span class="lineno">  244</span><span class="comment">    ///\brief  Sets the vector of hyper-parameters.</span></div>
<div class="foldopen" id="foldopen00245" data-start="{" data-end="}">
<div class="line"><a id="l00245" name="l00245"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_s_g_d_trainer.html#a60e89b698e5ff7d10ad7c613e369f0ac">  245</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_kernel_s_g_d_trainer.html#a60e89b698e5ff7d10ad7c613e369f0ac" title="Sets the vector of hyper-parameters.">setParameterVector</a>(RealVector <span class="keyword">const</span>&amp; newParameters){</div>
<div class="line"><a id="l00246" name="l00246"></a><span class="lineno">  246</span>        <span class="keywordtype">size_t</span> kp = <a class="code hl_variable" href="classshark_1_1_kernel_s_g_d_trainer.html#ada7a1e197b8d2721ac531c68988dfd17" title="pointer to kernel function">m_kernel</a>-&gt;<a class="code hl_function" href="classshark_1_1_i_parameterizable.html#aed1e8d1d4dbde387e2f6a25141ed3a20" title="Return the number of parameters.">numberOfParameters</a>();</div>
<div class="line"><a id="l00247" name="l00247"></a><span class="lineno">  247</span>        <a class="code hl_define" href="_exception_8h.html#a73abb5049a0168d72a48e72dda41708b">SHARK_ASSERT</a>(newParameters.size() == kp + 1);</div>
<div class="line"><a id="l00248" name="l00248"></a><span class="lineno">  248</span>        <a class="code hl_variable" href="classshark_1_1_kernel_s_g_d_trainer.html#ada7a1e197b8d2721ac531c68988dfd17" title="pointer to kernel function">m_kernel</a>-&gt;<a class="code hl_function" href="classshark_1_1_i_parameterizable.html#ad5e35d1a10ff36fa72ea787baa40e9ad" title="Set the parameter vector.">setParameterVector</a>(subrange(newParameters,0,kp));</div>
<div class="line"><a id="l00249" name="l00249"></a><span class="lineno">  249</span>        <a class="code hl_variable" href="classshark_1_1_kernel_s_g_d_trainer.html#a374547615c259b8b19ca846fac2a9753" title="regularization parameter">m_C</a> = newParameters.back();</div>
<div class="line"><a id="l00250" name="l00250"></a><span class="lineno">  250</span>        <span class="keywordflow">if</span>(<a class="code hl_variable" href="classshark_1_1_kernel_s_g_d_trainer.html#acc37f18ac6b0d7bf0a526f960ad4c4b6" title="should C be stored as log(C) as a parameter?">m_unconstrained</a>) <a class="code hl_variable" href="classshark_1_1_kernel_s_g_d_trainer.html#a374547615c259b8b19ca846fac2a9753" title="regularization parameter">m_C</a> = exp(<a class="code hl_variable" href="classshark_1_1_kernel_s_g_d_trainer.html#a374547615c259b8b19ca846fac2a9753" title="regularization parameter">m_C</a>);</div>
<div class="line"><a id="l00251" name="l00251"></a><span class="lineno">  251</span>    }</div>
</div>
<div class="line"><a id="l00252" name="l00252"></a><span class="lineno">  252</span><span class="comment"></span> </div>
<div class="line"><a id="l00253" name="l00253"></a><span class="lineno">  253</span><span class="comment">    ///\brief Returns the number of hyper-parameters.</span></div>
<div class="foldopen" id="foldopen00254" data-start="{" data-end="}">
<div class="line"><a id="l00254" name="l00254"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_s_g_d_trainer.html#a9acfcaf7fee65d405706e35c16d43bb0">  254</a></span><span class="comment"></span>    <span class="keywordtype">size_t</span> <a class="code hl_function" href="classshark_1_1_kernel_s_g_d_trainer.html#a9acfcaf7fee65d405706e35c16d43bb0" title="Returns the number of hyper-parameters.">numberOfParameters</a>()<span class="keyword"> const</span>{</div>
<div class="line"><a id="l00255" name="l00255"></a><span class="lineno">  255</span>        <span class="keywordflow">return</span> <a class="code hl_variable" href="classshark_1_1_kernel_s_g_d_trainer.html#ada7a1e197b8d2721ac531c68988dfd17" title="pointer to kernel function">m_kernel</a>-&gt;<a class="code hl_function" href="classshark_1_1_i_parameterizable.html#aed1e8d1d4dbde387e2f6a25141ed3a20" title="Return the number of parameters.">numberOfParameters</a>() + 1;</div>
<div class="line"><a id="l00256" name="l00256"></a><span class="lineno">  256</span>    }</div>
</div>
<div class="line"><a id="l00257" name="l00257"></a><span class="lineno">  257</span> </div>
<div class="line"><a id="l00258" name="l00258"></a><span class="lineno">  258</span><span class="keyword">protected</span>:</div>
<div class="line"><a id="l00259" name="l00259"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_s_g_d_trainer.html#ada7a1e197b8d2721ac531c68988dfd17">  259</a></span>    <a class="code hl_class" href="classshark_1_1_abstract_kernel_function.html">KernelType</a>* <a class="code hl_variable" href="classshark_1_1_kernel_s_g_d_trainer.html#ada7a1e197b8d2721ac531c68988dfd17" title="pointer to kernel function">m_kernel</a>;                     <span class="comment">///&lt; pointer to kernel function</span></div>
<div class="line"><a id="l00260" name="l00260"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_s_g_d_trainer.html#ada9d9dbb9f3abd4cbf09c53e0bb92fd6">  260</a></span>    <span class="keyword">const</span> <a class="code hl_class" href="classshark_1_1_abstract_loss.html">LossType</a>* <a class="code hl_variable" href="classshark_1_1_kernel_s_g_d_trainer.html#ada9d9dbb9f3abd4cbf09c53e0bb92fd6" title="pointer to loss function">m_loss</a>;                   <span class="comment">///&lt; pointer to loss function</span></div>
<div class="line"><a id="l00261" name="l00261"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_s_g_d_trainer.html#a374547615c259b8b19ca846fac2a9753">  261</a></span>    <span class="keywordtype">double</span> <a class="code hl_variable" href="classshark_1_1_kernel_s_g_d_trainer.html#a374547615c259b8b19ca846fac2a9753" title="regularization parameter">m_C</a>;                               <span class="comment">///&lt; regularization parameter</span></div>
<div class="line"><a id="l00262" name="l00262"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_s_g_d_trainer.html#a2222af2e7de2a6c68b18678f0fff1cff">  262</a></span>    <span class="keywordtype">bool</span> <a class="code hl_variable" href="classshark_1_1_kernel_s_g_d_trainer.html#a2222af2e7de2a6c68b18678f0fff1cff" title="should the resulting model have an offset term?">m_offset</a>;                            <span class="comment">///&lt; should the resulting model have an offset term?</span></div>
<div class="line"><a id="l00263" name="l00263"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_s_g_d_trainer.html#acc37f18ac6b0d7bf0a526f960ad4c4b6">  263</a></span>    <span class="keywordtype">bool</span> <a class="code hl_variable" href="classshark_1_1_kernel_s_g_d_trainer.html#acc37f18ac6b0d7bf0a526f960ad4c4b6" title="should C be stored as log(C) as a parameter?">m_unconstrained</a>;                     <span class="comment">///&lt; should C be stored as log(C) as a parameter?</span></div>
<div class="line"><a id="l00264" name="l00264"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_s_g_d_trainer.html#a0230c6c90039fd6dbc20dd5192c69c8b">  264</a></span>    std::size_t <a class="code hl_variable" href="classshark_1_1_kernel_s_g_d_trainer.html#a0230c6c90039fd6dbc20dd5192c69c8b" title="number of training epochs (sweeps over the data), or 0 for default = max(10, C)">m_epochs</a>;                     <span class="comment">///&lt; number of training epochs (sweeps over the data), or 0 for default = max(10, C)</span></div>
<div class="line"><a id="l00265" name="l00265"></a><span class="lineno">  265</span> </div>
<div class="line"><a id="l00266" name="l00266"></a><span class="lineno">  266</span>    <span class="comment">// size of cache to use.</span></div>
<div class="line"><a id="l00267" name="l00267"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_s_g_d_trainer.html#a3266b159eb84e746be2d35ff719c26bd">  267</a></span>    std::size_t <a class="code hl_variable" href="classshark_1_1_kernel_s_g_d_trainer.html#a3266b159eb84e746be2d35ff719c26bd">m_cacheSize</a>;</div>
<div class="line"><a id="l00268" name="l00268"></a><span class="lineno">  268</span> </div>
<div class="line"><a id="l00269" name="l00269"></a><span class="lineno">  269</span>};</div>
</div>
<div class="line"><a id="l00270" name="l00270"></a><span class="lineno">  270</span> </div>
<div class="line"><a id="l00271" name="l00271"></a><span class="lineno">  271</span> </div>
<div class="line"><a id="l00272" name="l00272"></a><span class="lineno">  272</span>}</div>
<div class="line"><a id="l00273" name="l00273"></a><span class="lineno">  273</span><span class="preprocessor">#endif</span></div>
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